1. Technical Field
The present invention pertains to the field of elevator control. More particularly, the present invention pertains to varying weights of a neural network used to calculate the remaining response time for an elevator to service a hall call.
2. Description of Related Art
Elevator dispatching systems use a number of factors in determining which car is the most appropriate to service a request (hall call). Since conditions are constantly changing, such systems evaluate and reevaluate the best car to serve a hall call "on-the-fly", so that a final selection need not be made until the last possible moment. See, e.g., U.S. Pat. No. 4,815,568 to Bittar. The control parameter remaining response time (RRT) may be defined as the estimated time for a car to travel from its current position to the floor with outstanding hall call. This control parameter is a critical element in determining which car is the most appropriate car to service a request. See, e.g., U.S. Pat. No. 5,146,053 to Powell.
Artificial neural networks have recently been applied to the problem of estimating RRT. See, e.g., U.S. Pat. No. 5,672,853 to Whitehall et al. Neural networks have proven useful in estimating RRT, but in implementations so far, the neural networks have had to be trained before being put to use. Usually, training is performed off-line, before the elevator is put into operation. Data is logged during the operation of the elevator system without the neural network, and then used to train the neural network for future use in estimating RRT. Once the neural network is put into operation with the elevator, the neural network is static. In other words, if the building population changes or traffic patterns change, the neural network will not adapt unless it is taken off line and retrained.
What is needed is a way of implementing a neural network so that it can be trained continuously, allowing it to adjust to changes in how the elevator is used.